Gluten-related disorders are prevalent worldwide, especially in developing countries. These diseases have similar clinical manifestations. Although they are characterized by a specific pathophysiological response to ingestion of gluten, differential diagnosis of these disorders is very challenging. Failure to diagnose and treat these disorders at an early stage, can lead to irreparable health and cost damages. Gluten-Related Disorders: Diagnostic Approaches, Treatment Pathways, and Future Perspectives solves this problem by reporting up-to-date scientific findings that allow for faster, more accurate diagnosis of gluten-related disorders as well as guidance on treatment options. By identifying the distinctive features of each of the gluten-related disorders, the chapters lead readers to optimal diagnosis and treatment options as well as better research designs for future research studies on the pathogenesis of these disorders. This reference provided by experts in the field is perfect for researchers, scientists, and medical practitioners who are involved in addressing gluten-related disorders.

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.

This book discusses compressive sensing in the presence of side information. Compressive sensing is an emerging technique for efficiently acquiring and reconstructing a signal. Interesting instances of Compressive Sensing (CS) can occur when, apart from sparsity, side information is available about the source signals. The side information can be about the source structure, distribution, etc. Such cases can be viewed as extensions of the classical CS. In these cases we are interested in incorporating the side information to either improve the quality of the source reconstruction or decrease the number of samples required for accurate reconstruction. In this book we assume availability of side information about the feasible region. The main applications investigated are image deblurring for optical imaging, 3D surface reconstruction, and reconstructing spatiotemporally correlated sources. The author shows that the side information can be used to improve the quality of the reconstruction compared to the classic compressive sensing. The book will be of interest to all researchers working on compressive sensing, inverse problems, and image processing.